Name
Abstract | ||
|---|---|---|
The goal of this paper is to evaluate the DisTrad sharpening technique for deriving land surface temperatures over urban areas. While the original DisTrad technique is based on the correlation between land surface temperature and NDVI, this study evaluates the performance of DisTrad over different land covers by analysing the correlation between land surface temperature and 15 different indices: BASVI, R, B, NDWI, NDBaI, SVI, SAVI, NDBI, NDSI, UI, FC, VC, V. IBI, NDVI. In addition, we have analysed the correlation between land surface temperature and impervious percentage. These indices and land surface temperature were derived from a Landsat 7 ETM+ image of 2001 covering the city of Dublin. It is concluded that for most indices selecting 25% of the pixels with the lowest coefficient of variance increases the correlation between the index and the land surface temperature. Results show that the DisTrad technique in combination with impervious percentage sharpens urban areas at 30 m resolution most successfully. Although vegetation cover was high during acquisition of the image, the use of impervious percentage showed improved results compared to NDVI. This allows an improved estimation of spatial patterns of urban heat islands. (c) 2012 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.jag.2012.05.010 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
Impervious surface percentage index,Thermal remote sensing,Thermal sharpening,DisTrad,Spectral indexes,Urbanization mapping,Urban heat island UHI | Urban heat island,Sharpening,Impervious surface,Land surface temperature,Thermal,Hydrology,Remote sensing,Normalized Difference Vegetation Index,Pixel,Spatial ecology,Geography | Journal |
Volume | ISSN | Citations |
19 | 0303-2434 | 4 |
PageRank | References | Authors |
0.58 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wiesam Essa | 1 | 16 | 1.79 |
| Boud Verbeiren | 2 | 21 | 3.48 |
| Johannes van der Kwast | 3 | 29 | 4.96 |
| Tim Van de Voorde | 4 | 57 | 8.78 |
| Okke Batelaan | 5 | 36 | 6.89 |